AI for Thermal Energy Science Workshop

April 17-18, 2023, Irvine CA, USA

The workshop is created to enhance conversations across multiple disciplines to discuss the role of advanced AI and machine learning (ML) techniques in thermal energy science and applications. Heat transfer is central to energy conversion and thermal management systems, where the ability to understand and design thermal energy systems is critical to future reductions in carbon emissions. A long-standing fundamental challenge that prevents a comprehensive understanding of heat transfer phenomena is the lack of diagnostic, analytic, and predictive tools that can investigate fundamental investigations of flows near liquid-vapor boundaries or temperature maps. Recent advances in computer vision and machine learning present an exciting opportunity to overcome these challenges. Computer vision methods may be used to measure thermal or interfacial features and to train machine learning models that couple high-resolution vision information with lower fidelity measurements and provide inferences about heat transfer phenomena and new physical insights. The vision information may also be coupled with physics-informed models and first principles. The purpose of this workshop is to initiate conversations between multiple disciplines, e.g., computer science, data science, graphic science, and heat transfer community, to further discuss how AI tools advance the fundamental understanding of heat transfer physics and predict them.

See the program here.